Systems and methods for monocular airborne object detection

ABSTRACT

Systems and methods for airborne object detection using monocular sensors are provided. In one embodiment, a system for detecting moving objects from a mobile vehicle comprises: an imaging camera; a navigation unit including at least an inertial measurement unit; and a computer system coupled to the image camera and the navigation unit. The computer system executes an airborne object detection process algorithm, and calculates a transformation between two or more image frames captured by the imaging camera using navigation information associated with each of the two or more image frames to generate a motion compensated background image sequence. The computer system detects moving objects from the motion compensated background image sequence.

BACKGROUND

Unmanned automated vehicles (UAV's) need to sense and avoid the presenceand locations of obstacles in order to safely navigate paths to completetheir missions. Airborne UAV's further have the need to detect otherairborne objects from their surrounding environments. Power and weightconstraints, however, limit what sensing technologies may be employed inairborne UAV's. Stereoscopic image processing requires duplicity ofimage sensors in order to capture images that may be used to determine arange of an airborne object. In addition, in many applications, therequired separation of the image sensors for a desired depth resolutionusing stereoscopic images exceeds the available dimensions (for example,wingspans). Single sensor technologies, such as Radar, Lidar andmillimeter wave radar (as well as the power sources required to powersuch equipment) are typically too heavy for use in lightweight airborneUAV designs.

For the reasons stated above and for other reasons stated below whichwill become apparent to those skilled in the art upon reading andunderstanding the specification, there is a need in the art for improvedsystems and methods for airborne object detection using monocularsensors.

SUMMARY

The Embodiments of the present invention provide methods and systems formonocular airborne object detection and will be understood by readingand studying the following specification.

In one embodiment, a system for detecting moving objects from a mobilevehicle comprises: an imaging camera; a navigation unit including atleast an inertial measurement unit; and a computer system coupled to theimage camera and the navigation unit. The computer system executes anairborne object detection process algorithm, and calculates atransformation between two or more image frames captured by the imagingcamera using navigation information associated with each of the two ormore image frames to generate a motion compensated background imagesequence. The computer system detects moving objects from the motioncompensated background image sequence.

DRAWINGS

Embodiments of the present invention can be more easily understood andfurther advantages and uses thereof more readily apparent, whenconsidered in view of the description of the preferred embodiments andthe following figures in which:

FIG. 1A is a block diagram illustrating a monocular airborne objectdetection system of one embodiment of the present invention;

FIG. 1B is a block diagram illustrating image frames and navigationinformation for a monocular airborne object detection system of oneembodiment of the present invention;

FIG. 2A is a diagram illustrating monocular airborne object detection ofone embodiment of the present invention;

FIG. 2B is a diagram illustrating monocular airborne object detection ofone embodiment of the present invention;

FIG. 3 is a diagram illustrating monocular airborne object detection asimplemented in a plurality of unmanned automated vehicles of oneembodiment of the present invention; and

FIG. 4 is a flow chart illustrating a method for monocular airborneobject detection of one embodiment of the present invention.

In accordance with common practice, the various described features arenot drawn to scale but are drawn to emphasize features relevant to thepresent invention. Reference characters denote like elements throughoutfigures and text.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof, and in which is shown byway of specific illustrative embodiments in which the invention may bepracticed. These embodiments are described in sufficient detail toenable those skilled in the art to practice the invention, and it is tobe understood that other embodiments may be utilized and that logical,mechanical and electrical changes may be made without departing from thescope of the present invention. The following detailed description is,therefore, not to be taken in a limiting sense.

Embodiments of the present invention address the needs ofself-navigating vehicles to be able to detect both moving and staticobjects within their surroundings while avoiding the weight and powerrequirements typically associated with previously known object detectionschemes. Although flying self-navigating vehicles are primarilydiscussed by example below, one of ordinary skill in the art readingthis specification would appreciate that embodiments of the presentinvention are not limited to flying self-navigating vehicles. Otherembodiments including land and water self-navigating vehicles arecontemplated as within the scope of embodiments of the presentinvention, as well and as vehicles piloted remotely or by a pilot onboard. Embodiments of the present invention achieve detection of movingobjects (such as airborne objects, for example) by joining a pluralityimages captured from a vehicle mounted camera with vehicle inertial datato produce image sequences having a motion compensated background thatenable the detection of moving objects. As used herein, the term“camera” is a generic term for any device that takes an image using anyspectrum to which the camera is sensitive, and projecting the observedspace onto a two-dimensional plane. For example, a camera, as the termis used herein, may be sensitive to all or part of the light spectrumvisible to humans, or alternatively, spectrums of higher or lowerfrequency. Alternatively, a camera, as the term is used herein, alsoincludes a device that produces a projection of observed space onto atwo-dimensional Riemannian manifold based on a form of energy other thanphotonic light energy.

FIG. 1A is a block diagram illustrating a system 100 for the detectionof airborne objects of one embodiment of the present invention. System100 includes an imaging camera 110, a navigation unit 112 including atleast an inertial measurement unit (IMU) 115, and a computer system 120performing an analysis of the information acquired by the imaging camera110 and the navigation unit 112 as described below. In one embodiment,system 100 is incorporated into an unmanned automated vehicle (UAV) suchas shown at 105.

In operation, imaging camera 110 captures a plurality of image frames.For each image frame captured, IMU 115 captures the inertial measurementdata (i.e., accelerometer and gyroscopic data) or the associatednavigation information (i.e. inertial navigation system information) forvehicle 105 at the time each frame is captured. For the purpose of thisspecification, both will be called “navigation information” but it is tobe understood that any type of a motion description can be used in theterm used is therefore not to be taken as limiting and scope. Asillustrated in FIG. 1B generally at 150, for each of the plurality ofimage frames (Frame 1 to Frame n), there is associated navigationinformation for that reference frame. Computer system 120 compensatesfor the movement of imaging camera 110 using data from the IMU 115 inorder to generate an image referred to herein as a motion compensatedbackground image sequence. From the motion compensated background imagesequence, computer system 120 can differentiate moving objects fromstatic background objects.

Using the navigation information from the IMU 115, computer system 120calculates an appropriate transformation, for example in the form of afundamental matrix. A fundamental matrix acts as a transformation matrixbetween any two image frames and can be calculated from the associatednavigation information for the two image frames. A fundamental matrix,when applied to a first image frame, will generate an image projectionrepresenting how a scene captured by the first image frame should appearfrom the perspective of the camera at the point in time when the secondimage frame is captured, assuming that all the objects within the sceneare located at the apparent affinity compared to the baseline betweenthe camera frames. A fundamental matrix thus represents how the camera110 has rotated between taking the first and second image frames.Embodiments of the present invention create a motion compensatedbackground image sequence by overlapping one or more reprojected framesonto a selected one of the plurality of captured image frame. In otherembodiments, the transformation is computed using an essential matrix,or quarternion math, transformation vector field, or otherrepresentation.

FIG. 2A provides an example 200 of a frame reprojection of oneembodiment of the present invention for creating a motion compensatedbackground image sequence. Camera 110 captures a first image (F1) showngenerally at 210 while navigation system 112 measures associatednavigation information (N1) for that point in time when F1 is captured.Camera 110 subsequently captures second image (F2) shown generally at212 while associated navigation information (N2) is measured bynavigation system 112. Fundamental Matrix FM_(1,2) (shown generally at214) is calculated from F1, N1, F2 and N2 using one of any applicableknown means for calculating a fundamental matrix. For example, in oneembodiment, a fundamental matrix is calculated usingF=K^(1-T)[t]_(x)RK⁻¹, where []_(x) is the matrix representation of thecross-product (a multiplication with skew-symmetric matrix), the R isthe rotation matrix, t is the translation vector and K is the cameraintrinsic calibration matrix. One available reference that discussescalculation of a fundamental matrix is provided by Hartley, R., andZisserman, A., Multiple View Geometry, Vol. 4, Cambridge UniversityPress, 2000, which is herein incorporated by reference. ApplyingFM_(1,2) to the first image F1 produces F1′ (shown generally at 220), areprojection of the first image F1 as it should appear from the vantagepoint of camera 110 when image frame F2 was captured, provided all theobjects within the scene are located at the apparent infinity. Combiningthe results of F1′ and F2 produces the motion compensated backgroundimage sequence 222. In the motion compensated background image sequence,the reprojection of any static object in F1′ should overlap with itselfin F2. Every static object at a first position in the first image frame,after being transformed into the reprojection of frame one using thefundamental matrix, will end up reprojected onto itself on the secondimage frame as viewed in the motion compensated background imagesequence, assuming that all background objects are located at anapparent infinity. A moving object or an object closer than apparentinfinity (indicated generally at 230) in contrast, will instead appearat varying locations, as shown generally at 235, in the motioncompensated background image sequence 222. The reprojection of an objectin motion in F1′ will not overlap with itself in F2. Thus an object inmotion 230 in F1′ appears as it would have appeared to the camera fromthe vantage point of F2, but at the time F1 was captured. The result isthat F1′ depicts the moving object 230's previous position while F2depicts the object 230's most recent position. For this reason, when F1′and F2 are overlaid, any object in motion will appear twice. In oneembodiment, objects that appear more than once in the motion compensatedbackground image sequence are identified by calculating the differencebetween F1′ and F2 (by using an XOR function, for example) as showngenerally at 224. This will reveal objects that appear in one frame, orthe other, but not both, as shown generally at 236. The resultidentifies features from each image frame that did not follow thetransform and are therefore not static or located closer than apparentinfinity.

Although, FIG. 2A illustrates identification of objects in motion usingtwo images, as shown in FIG. 2B, any plurality of images may be used.For example, image frames (F1 to Fn) with their associated navigationinformation (N1 to Nn) are captured as shown generally at 255. Usingframe Fn as the target frame to be projected into, fundamental matrixFM_(1,n) is calculated and reprojection F′_(1,n) is generated byapplying FM_(1,n) to F1. In the same way frames F2 . . . Fn-1 arereprojected into, F′_(2,n) . . . F′_(n-1,n). Calculation of fundamentalmatrices FM_(1,n) to FM_(n-1,n) respectively reprojects each frame intoa the selected target image frame Fn. The reprojections F′_(1,n) toF′_(n-1,n) (shown generally at 260) and Fn (as shown at 261) areprocessed (shown generally at 265) to generate a motion compensatedbackground image sequence 270. As described above, objects located atthe virtual infinity (i.e. background objects) will be reprojected ontothemselves. Moving objects, in contrast, will instead appear at multiplelocations. Applying an bitwise subtraction or a XOR function to the setof F′_(1,n) to F′_(n-1,n) and Fn images allows differentiation of movingobjects from static objects.

Once moving objects are identified, computer system 120 estimates aposition (in 3-dimensions) for one or more of the moving objects fromthe captured images using a known methods, such as, but not limited to ageneralized 3D-reprojection or a structure-from-motion techniques knownto those of ordinary skill in the art. That is, computer system 120 notonly identifies that there is an airborne moving object represented bycertain pixel(s) within an image, it also determines where the movingobject is located in 3-dimensional space. Structure-from motiontechniques incorporate knowledge of the camera's trajectory along withthe sequence of the detected objects in the camera images to calculatewhere the object is in 3-dimensional space, in a similar manner asstereoscopic reprojections utilize the relative distance between camerasto calculate the depth of an object. In one embodiment of the presentinvention, computer system 120 acquires depth information from onecamera by capturing images at different times and positions withknowledge of the UAV's trajectory information. This depth information isapplied to the specific airborne objects identified above to determinethe relative position (i.e., with respect to the UAV's local frame ofreference) of the moving objects for purposes of collision avoidance orseparation assurance.

In one embodiment, navigation system 112 optionally includes a globalnavigation satellite system (GNSS) receiver 117 coupled to computersystem 120 to further augment the trajectory information available fordetermining the location of detected objects. By including GNSSaugmented trajectory information that identifies the UAV's trajectory asreference to a chosen reference frame (for example, global coordinates),computer system 120 can identify a moving airborne objects with respectthe UAV's local frame of reference, or with respect to the navigationframe. GNSS receiver 117 augments the navigation information availableto computer system 120 by providing the UAV's absolute position withrespect to the navigation frame. As would be appreciated by one ofordinary skill in the art upon reading this specification, othernavigation systems besides GNSS may be used to provide this information.Accordingly in one embodiment, navigation system 112 includes one ormore other navigation sensors 119 to augment trajectory informationavailable for determining the location of detected moving objects.Further, other motion estimation technologies may be used to complementsthe results of the navigation system 112 to consequently increase theaccuracy of the solution provided by computer system 120.

In one embodiment, computer system 120 provides its solution in the formof a state vector for each detected moving object that describes atleast its estimated position. When navigation system 112 providessatellite based navigation information, the state vector calculated bycomputer system 120 can be referenced to the global navigation frame. Inone embodiment, one or more state vectors are provided to aguidance/flight control computer 125 coupled to computer system 120.Based on the information provided by the state vectors regarding thedetected moving objects, guidance/flight control computer 125 caninitiate evasive or mitigating adjustments to the flight course of theUAV. Optionally, the flight controller 125 can further transmit alarmmessages to ground based stations or other UAV's regarding the detectedmoving objects. In one embodiment, computer system 120 is furtherprogrammed to track the detected airborne objects and to provideestimates of additional states, like motion velocity vector, time tocollision or other states. In another embodiment, future trajectories ofdetected airborne objects are further extrapolated to estimate acollision probability.

As would be appreciated by one of ordinary skill in the art upon readingthis specification, one of the biggest challenges for airborne objectdetection scheme that rely on motion detection is the detection ofobjects on a direct collision course with the observer. When airborneobjects are both flying straight with a constant speed and on acollision course, the observation angle of the airborne object (asviewed from each of the airborne objects) does not change. Consequently,the position of the object within a motion compensated background imagesequence will remain the same and thus appear to be a fixed backgroundobject rather than a moving airborne object. One clue provided from themotion compensated background image sequence is that the size of theobject within the image will increase with each sequential image.However, the magnitude of this change may not be detectable in time totake action.

To address the detection of objects on a direct collision course, oneembodiment of the present invention introduces an artificial trajectoryexcitation into the UAV flight path to achieve a slight difference inthe observation perspective of sequential images captured by the camera.This excitation may include altering the linearity of the UAV's flightpath using the natural modes of aircraft to motion or by varying thespeed of the UAV, for example. The different observation perspectivesenable establishment of a baseline (a distance that is perpendiculartowards the UAV's axis of motion) which is needed to estimate thedistance to the object. In one embodiment, such deviations in the UAVflight path are periodically introduced by the flight computer 125 sothat computer system 120 can look for the potential presence of objectson a collision course with the UAV. In one embodiment, the frequency,amplitude, and direction of deviations is increased once a potentialcollision course object is identified, to establish a better baseline inthe respective direction.

In some embodiments, the UAV further includes a stearable laser rangefinder 111. In such embodiment, once a potential collision threat isidentified, the UAV may employ the stearable laser range finder toverify the existence of the detected threat and measure the distance tothe detected object. In other embodiments, the objects detected usingthe described method are fused with detections from other sensorfamilies (radar, transponder, etc.), to increase the integrity of thesolution by taking advantage of complementary properties. In respectiveembodiments, this sensor fusion is based on an Extended Kalman Filter,Unscented Kalman Filter and a Particle Filter, respectively. The samefilter is then used as an estimator to provide the extended motion modalstates for the detected objects

The descriptions above provide idealized examples where backgroundobjects are assumed to be located at virtual infinity. However, this isnot an assumption that is always valid. For example, when a UAV isflying close to the ground, images of the ground captured by the cameracannot be considered to be at virtual infinity. Such deviations from theideal situation introduce scatter into the motion compensated backgroundimage sequence. To the degree that such scatter is trivial when comparedthe motion of detected airborne objects of interest, it may be simplyignored as long as it does not exceed a threshold predetermined based onthe mission of the UAV. Alternatively, a background closer than virtualinfinity may be addressed in one of several ways.

In one embodiment, computer system 120 divides each captured image frameinto smaller segments and refines the motion of that segment using theacquired visual content aided by the associated navigation informationfor the image. Assuming the UAV is close to the ground, as the cameramoves between successive frames it will observe the background as aclose and moving object. Processing a local patch of neighboring imagesegments and using the trajectory information (direction of translationand a rotation) computer system 120 determines which direction thebackground (e.g. the ground) for this patch of image segments has movedwithin the segments. Features in the image frame within segments thatcapture the ground, and appear moving at the same speed and direction asthe ground in that patch, are deemed to be part of the background andnot airborne objects.

Alternatively in one embodiment, computer system 120 dynamically variesthe frame rate of images being captured by camera 110, and/or discardssome frames. For example, in one embodiment, computer system 120 adjuststhe image frame rate for camera 110 based on the speed of the UAV (whichis, for example, known from information provided by INS 115) so that thebackground will appear to be at virtual infinity. Any object closer thanthe background is not at infinity and will thus be visible whenreprojected into the motion compensated background image sequence. Inone embodiment, the amount of clutter in the motion compensatedbackground image sequence versus the base image frame is used as thebasis for increasing or decreasing the image frame rate. For example,given two image frames and their associated navigation information,calculate a fundamental matrix. From the fundamental matrix, generate areprojection of the first image. Then find the difference between thereprojection of the first image and the second image (by subtraction orXOR, for example). Given the amount of clutter you can discern from thisdifference determine if the sampling periods are short enough or not tosatisfy the context of the background being at a virtual infinity. Inanother alternate embodiment, other scheme known to those of ordinaryskill in the art, such as optical flow compensations, can be used tocompensate for having static background objects closer than virtualinfinity in captured image frames.

In one embodiment, when multiple UAV's are flying in a coordinatedmanner (e.g. such as in formation or in a loosely coordinated swarm)then two or more UAV's can share information via a wireless data linkand use the shared information to calculate the distance to an object.FIG. 3 illustrates one such embodiment of the present inventioncomprising a plurality of UAV's (shown generally at 305). In oneembodiment, each of the UAV's 305 are equipped with a system for thedetection of airborne objects such as system 100 shown in FIG. 1. Forexample, in one embodiment, a first UAV 310 identifies an airborneobject 320 and assigns a unique identifier to that object 320. UAV 310then calculates a state vector for that object referenced to the globalnavigation frame. A second UAV 315 on a collision course with theairborne object 320 can wirelessly import that state vector availablefrom the first UAV (shown generally by the data link at 325) tocalculate its own distance to the object 320. Alternatively, in anotherembodiment, the second UAV 315 can import the raw image data captured bythe first UAV 310, along with the navigation information associated withthe image data, to calculate its own state vector for the object 320 andthus determine its distance to object 320.

Several means are available to implement the systems and methods of thecurrent invention as discussed in this specification. These meansinclude, but are not limited to, digital computer systems,microprocessors, general purpose computers, programmable controllers andfield programmable gate arrays (FPGAs). For example, in one embodiment,computer system 120 is implemented by an FPGA or an ASIC, or an embeddedprocessor. Therefore other embodiments of the present invention areprogram instructions resident on computer readable media which whenimplemented by such means enable them to implement embodiments of thepresent invention. Computer readable media include any form of aphysical computer memory device. Examples of such a physical computermemory device include, but is not limited to, punch cards, magneticdisks or tapes, optical data storage system, flash read only memory(ROM), non-volatile ROM, programmable ROM (PROM), erasable-programmableROM (E-PROM), random access memory (RAM), or any other form ofpermanent, semi-permanent, or temporary memory storage system or device.Program instructions include, but are not limited to computer-executableinstructions executed by computer system processors and hardwaredescription languages such as Very High Speed Integrated Circuit (VHSIC)Hardware Description Language (VHDL).

FIG. 4 is a flow chart for a method for image based moving objectdetection for storage on a computer readable media device. The methodbegins at 410 with capturing two or more images of surrounding scenesaround a self-navigating vehicle. The method proceeds to 420 withmeasuring navigation information associated with the two or more imagesusing inertial sensors. The method proceeds to 430 with calculating afirst transformation (such as, for example, a fundamental matrix)between a first image frame and a second image frame of the two or moreimage frames, using the navigation information associated with the twoor more images. A fundamental matrix acts as a transformation betweenany two image frames and is calculated from the associated navigationinformation for the two image frames. A fundamental matrix, when appliedto a first image frame, will generate an image projection representinghow objects located at an apparent infinity in a scene captured by thefirst image frame should appear from the perspective of the camera atthe point in time when the second image frame is captured. A fundamentalmatrix thus represents how the camera 110 has rotated between taking thefirst and second image frames. The method proceeds to 440 withgenerating a motion compensated background image sequence based onapplying the first transformation to reproject the first image frameinto the second image frame. The method proceeds to 450 with detectingmoving objects from the motion compensated background image sequence. Inthe motion compensated background image sequence, any static objectlocated at apparent infinity in the first image frame should overlapwith itself when reprojected onto the second image frame. That is, everystatic object located at apparent infinity in a first position in thefirst image frame, after being transformed into the reprojection of thefirst image frame using the fundamental matrix, will end up reprojectedonto itself on the second image frame as viewed in the motioncompensated background image sequence. A moving object or an objectcloser than apparent infinity, in contrast, will instead appear atmultiple locations. The method proceeds to 460 with estimating positioninformation for moving objects around the self-navigating vehicle basedon the motion compensated background image sequence and the navigationinformation. In one embodiment, generalized 3D-reprojection orstructure-from motion techniques incorporate knowledge of the camera'strajectory along with the sequence of the detected objects in the cameraimages to calculate where an object is in 3-dimensional space, in asimilar manner as stereoscopic reprojections utilize the relativedistance between cameras to calculate the depth of an object. In oneembodiment of the present invention, estimating position includesacquiring depth information from one camera by capturing images atdifferent times and positions with knowledge of the self-navigatingvehicle's trajectory information. This depth information is applied tothe specific moving objects identified above to determine theirpositions (i.e., with respect to either the local frame of reference ora navigation frame) for purposes of collision avoidance. Thus, in oneembodiment, the method proceeds to 470 with altering a course of theself-navigating vehicle based on the position information.

Although specific embodiments have been illustrated and describedherein, it will be appreciated by those of ordinary skill in the artthat any arrangement, which is calculated to achieve the same purpose,may be substituted for the specific embodiment shown. This applicationis intended to cover any adaptations or variations of the presentinvention. Therefore, it is manifestly intended that this invention belimited only by the claims and the equivalents thereof.

1. A system for detecting moving objects from a mobile vehicle, thesystem comprising: an imaging camera; a navigation unit including atleast an inertial measurement unit; and a computer system coupled to theimage camera and the navigation unit; wherein the computer systemexecutes an airborne object detection process algorithm, and calculatesa transformation between two or more image frames captured by theimaging camera using navigation information associated with each of thetwo or more image frames to generate a motion compensated backgroundimage sequence; wherein the computer system detects moving objects fromthe motion compensated background image sequence; wherein the computersystems reprojects at least one image frame of the two or more imagesframes into a target image frame selected from the two or more imageframes; and wherein the computer system outputs a state vector for eachmoving object identified from the motion compensated background imagesequence, wherein the state vector describes at least an estimatedposition.
 2. The system of claim 1, further comprising a laser rangefinder coupled to the computer system, the laser range finder providinga distance measurement to one or more detected objects.
 3. The system ofclaim 1, wherein the transformation is computed using one or more of afundamental matrix, an essential matrix, or quarternion math.
 4. Thesystem of claim 1, wherein the state vector further describes a velocityvector, and a distance.
 5. The system of claim 4, wherein the statevector is estimated using at least one of a Particle Filter, an ExtendedKalman Filter or an Unscented Kalman Filter.
 6. The system of claim 1,wherein the computer tracks one or more of the moving objects using atleast one of a Particle Filter, an Extended Kalman Filter or anUnscented Kalman Filter.
 7. The system of claim 1, the navigation unitfurther including a navigation system providing global navigation frametrajectory information to the computer system; and wherein the computerestimates a position relative to a navigation frame for one or moremoving objects identified from the motion compensated background imagesequence based on the trajectory information.
 8. The system of claim 1,further comprising: a guidance/flight control system coupled to thecomputer system, wherein the flight control system makes adjustments toa flight course based on information regarding moving objects detectedfrom the motion compensated background image sequence.
 9. The system ofclaim 8, wherein the guidance/flight control system introducesartificial trajectory excitations.
 10. The system of claim 1, whereinthe computer system processes one or more local patches of neighboringimage segments to identify background motion for static objects locatedcloser than virtual infinity.
 11. The system of claim 1, wherein thecomputer system varies a frame rate of image frames captured by theimaging camera based on clutter identified in a motion compensatedbackground image sequence.
 12. An airborne detection system for anaircraft, the airborne detection system comprising: an imaging camera;an inertial measurement navigation unit generating navigationinformation; a computer system coupled to receive information from theimage camera, the inertial measurement navigation unit; a globalnavigation satellite system receiver providing trajectory information tothe computer system; and a guidance/flight control system coupled to thecomputer; wherein the computer system, based on an airborne objectdetection process algorithm, calculates a transformation between two ormore image frames captured by the imaging camera using navigationinformation associated with each of the two or more image frames togenerate a motion compensated background image sequence; wherein thecomputer system detects moving objects from the motion compensatedbackground image sequence and is configured to determine positioninformation for moving objects detected from the motion compensatedbackground image sequence; and wherein the flight controller isconfigured to alter a flight course for the aircraft based on theposition information.
 13. The system of claim 12, wherein thetransformation is computed using one or more of a fundamental matrix, anessential matrix, or quarternion math.
 14. The system of claim 12,wherein the computer system reprojects at least a first image frame intoa target image frame to generate the motion compensated background imagesequence.
 15. The system of claim 12, wherein computer system detectsmoving objects from the motion compensated background image sequencebased on images of objects appearing more than once in the motioncompensated background image sequence.
 16. The system of claim 12,wherein the computer outputs a state vector to the flight controller foreach moving object identified from the motion compensated backgroundimage sequence, wherein the state vector describes at least an estimatedposition, a velocity vector, and a distance.
 17. The system of claim 12,wherein the computer system compensates for static objects appearing inthe motion compensated background image sequence located closer thanapparent infinity.
 18. A computer readable media device havingcomputer-executable instructions for a method for image based movingobject detection, the method comprising: capturing two or more images ofsurrounding scenes around a self-navigating vehicle; measuringnavigation information associated with the two or more images usinginertial sensors; calculating a first transformation between a firstimage frame and a second image frame of the two or more image frames,using the navigation information associated with the two or more images;generating a motion compensated background image sequence based onapplying the first fundamental matrix to reproject the first image frameinto the second image frame; detecting moving objects from the motioncompensated background image sequence; estimating a position informationfor moving objects around the self-navigating vehicle based on themotion compensated background image sequence and the navigationinformation.
 19. The computer readable media device of claim 18, themethod further comprising: introducing artificial trajectory excitationsto the course of the self-navigating vehicle
 20. The computer readablemedia device of claim 18, the method further comprising: trackingdetected airborne objects; and providing an estimates of a one or bothof a motion velocity vector and a time to collision for the detectedairborne objects.